Neutral theory proposes that some macroscopic biodiversity patterns can be explained in terms of drift, speciation and immigration, without invoking niches. There are many different varieties of neutral model, all assuming that the fitness of an individual is unrelated to its species identity. Variants that are spatially explicit provide a means for making quantitative predictions about spatial biodiversity patterns.
We present software packages that make spatially explicit neutral simulations straightforward and efficient. The packages allow the user to customize both dispersal and landscape structure in a wide variety of ways. We provide a Python package pycoalescence and a functionally equivalent R package rcoalescence. In both packages, the core routines are written in C++ and make use of coalescence methods to optimize performance.
We explain the technical details of the packages and give examples for their application, with a particular focus on two scenarios of ecological and evolutionary interest: a landscape with habitat fragmentation, and an archipelago of islands.
Spatially explicit neutral models represent an important tool in ecology for understanding the processes of biodiversity generation and predicting outcomes at large scales. The effort required to implement these complex spatially explicit simulations efficiently has thus far been a barrier to entry. Our packages increase the accessibility of these models and encourage further investigation of the primary mechanisms underpinning biodiversity.